🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risk in terms of network, shell, obfuscation, and credential risks. However, its metadata risk score is moderate due to the package being new and the maintainer having limited history, raising suspicion.

Moderate metadata risk due to limited maintainer activity
Potential misuse of shell execution for Git operations and tests

Per-check LLM notes

Network: No network calls detected.
Shell: Shell execution is used for Git operations and running tests, which could be normal but requires further context to rule out misuse.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is new with limited activity and the maintainer has few packages, indicating potential risk.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

, *wildcards] completed = subprocess.run( run_cmd, cwd=project_root, env=env,
utRunError(msg) export = subprocess.run( [sys.executable, "-m", "mutmut", "export_cicd_stats
try: completed = subprocess.run( command, cwd=project_root,
try: completed = subprocess.run( ["git", "diff", "--name-only", f"{base_ref}...H
str(FIXTURE_ROOT / "src") subprocess.run( [ str(_venv_python()), "-m"

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "cheezd" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agentic-test-forge

Create a fully-functional mini-application that leverages the 'agentic-test-forge' package to streamline the testing and development process of a simple web scraper. This application will be named 'WebScraperQualityAssurance' and will serve as a demonstration of how 'agentic-test-forge' can enhance the quality assurance of software projects through automated testing, mutation testing, and Gherkin-based feature specifications.

The application should include the following components:
1. A basic web scraping module that fetches data from a specified website URL and extracts specific information such as titles, descriptions, and links.
2. Integration of 'agentic-test-forge' to set up CRAP (Constructive Reduction of Assurance Problems) metrics for assessing code quality, ensuring that the codebase remains maintainable and robust.
3. Implementation of mutation testing using 'agentic-test-forge' to identify potential weaknesses in the test suite coverage, thereby enhancing the reliability of the tests.
4. Use of Gherkin syntax to define the behavior and expected outcomes of the web scraping functionality, providing a clear specification for both developers and non-technical stakeholders.
5. A user-friendly interface that allows users to input a URL and specify which elements they want to scrape, with the output displayed in a structured format such as JSON or CSV.

Instructions for utilizing 'agentic-test-forge':
- Start by setting up a new Python environment and installing 'agentic-test-forge'.
- Define the CRAP metrics configuration within your project to ensure that the codebase adheres to high-quality standards.
- Write Gherkin scenarios that describe the intended behavior of the web scraping feature, focusing on edge cases and typical usage scenarios.
- Implement mutation testing using 'agentic-test-forge' to validate the effectiveness of your test suite, making adjustments to the tests as necessary based on the results.
- Continuously monitor and improve the quality of the code and tests throughout the development lifecycle, leveraging 'agentic-test-forge' for automated feedback and analysis.